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progressed from the top again. The best example of systematic sampling is
equal probability method.
Q34. What are Eigenvectors and Eigenvalues?
Eigenvectors are used for understanding linear transformations. In data
analysis, we usually calculate the eigenvectors for a correlation or covariance
matrix. Eigenvectors are the directions along which a particular linear
transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the
direction of eigenvector or the factor by which the compression occurs.
Q35. Can you cite some examples where a false positive is
important than a false negative?
Let us first understand what false positives and false negatives are.
False Positives are the cases where you wrongly classified a non-event
as an event a.k.a Type I error.
False Negatives are the cases where you wrongly classify events as
non-events, a.k.a Type II error.
Example 1: In the medical field, assume you have to give chemotherapy to
patients. Assume a patient comes to that hospital and he is tested positive for
cancer, based on the lab prediction but he actually doesn‘t have cancer. This is
a case of false positive. Here it is of utmost danger to start chemotherapy on
this patient when he actually does not have cancer. In the absence of
cancerous cell, chemotherapy will do certain damage to his normal healthy
cells and might lead to severe diseases, even cancer.
Example 2: Let‘s say an e-commerce company decided to give $1000 Gift
voucher to the customers whom they assume to purchase at least $10,000
worth of items. They send free voucher mail directly to 100 customers without
any minimum purchase condition because they assume to make at least 20%
profit on sold items above $10,000. Now the issue is if we send the $1000 gift
vouchers to customers who have not actually purchased anything but are
marked as having made $10,000 worth of purchase.